seeAI

Sondre U. Solstad

Visualizations of AI “thinking” in R

This package provides functions for visualizing AI / Machine Learning. Currently only one learning process is supported, namely LASSO regression with cross-validation.

Written by Sondre U. Solstad, Princeton University (ssolstad@princeton.edu). Send me an email if you find this package useful or want to suggest an improvement or feature.

LASSO coordinate descent and cross-validation

Installation instructions:

library(devtools)
install_github("sondreus/seeAI")

Example:

library(seeAI)
library(glmnet)

set.seed(1010)
n=1000;p=100
nzc=trunc(p/10)
x=matrix(rnorm(n*p),n,p)
beta=rnorm(nzc)
fx= x[,seq(nzc)] %*% beta
eps=rnorm(n)*5
y=drop(fx+eps)
px=exp(fx)
px=px/(1+px)
ly=rbinom(n=length(px),prob=px,size=1)

set.seed(1011)
cvob1 <- cv.glmnet(x,y)

animate_glmnet(cvob1)
animate_glmnet example gif

Arguments:

  • cv.glmnet An object of class ‘cv.glmnet’
  • plot.cvm Should cross-validation error be plotted? Defaults to TRUE.
  • plot.cv.folds Should cross-validation folds be plotted? Defaults to TRUE.
  • total.time Desired time of animation in seconds. Defaults to 10.
  • new.save Should this animation be saved as a new object rather than overwrite the preceeding animation? Defaults to TRUE.
  • save.html Save as HTML? Defaults to TRUE. If FALSE, saves GIF.
  • debug Only plot subset of lambda values? Defaults to FALSE.
  • debug.n If plotting subset of lambda values, sets number of values to plot. Defaults to 100.

Explanation and detail

Acknowledgements

This package relies on the glmnet package by Jerome Friedman, Trevor Hastie, Noah Simon and Rob Tibshirani, and the animation package by Yihui Xie.